EMG and GRF were used to evaluate the motor function 2 months post-operation, which were performed and recorded using the Vicon Motion Capture System (Oxford Metrics Limited, Oxford, UK) composed of six high-speed cameras. We adjusted the visual field of the system (Figure 1A Figure 1B Figure 1C
Vicon motion capture system
Manufactured by Oxford Metrics
Sourced in United Kingdom
The Vicon motion capture system is a professional-grade motion tracking technology designed for applications that require high-precision motion data. It utilizes advanced cameras and software to accurately capture the movement of objects or individuals in three-dimensional space. The system is capable of recording and analyzing complex kinematic data with a high degree of accuracy and reliability, making it a valuable tool for a wide range of industries and applications.
Automatically generated - may contain errors
Lab products found in correlation
Matlab, by MathWorks (5 mentions)
Labview, by National Instruments (1 mentions)
Matlab version 7, by MathWorks (1 mentions)
Force plate, by Kistler (1 mentions)
Force platform, by Kistler (1 mentions)
In ground force plates, by Bertec (1 mentions)
3d force platform, by Amti (1 mentions)
Force platform, by Amti (1 mentions)
23 protocols using vicon motion capture system
1
Gait Analysis via Motion Capture
2
Investigating Group Dynamics in Motion Capture
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Twelve participants (8 males and 4 females, aged 30 ± 7 years) volunteered to take part in this experiment. The participants were recruited through general advertisement within the research institute and local notice boards. The participants were randomly allocated to four groups of three participants. Two groups consisted of males only (aged 34 ± 11 and 32 ± 8 years), one group consisted of females only (aged 30 ± 7.5 years) and one group was mixed with 2 males and 1 female (aged 29 ± 5 years). Participants typically did not know each other before the experiment; however, this was not recorded. All participants had normal or corrected-to-normal vision and no known motor impairments that affected their walking ability. This study was carried out in accordance with the recommendations of the research institute. All participants gave their written informed consent in accordance with the declaration of Helsinki. The experiment took place in a motion capture laboratory with an 18 camera VICON motion capture system (Oxford Metrics Group Ltd., Oxford, UK) covering an interaction area of 13.5 × 17.5 m. Participants' trajectories were recorded with a sampling rate of 120 Hz using a retro-reflexive marker on each shoulder, the center of which was used to represent the participants' displacement. Additionally, we used a set of markers on a helmet to identify each participant.
3
Virtual Reality Treadmill Training for Gait Rehabilitation
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The VRTT was performed with the Gait Real-time Analysis Interactive Lab (GRAIL, Motek, Houten, The Netherlands) that is an immersive VR system for gait assessment and rehabilitation (Figure 1 B). It is equipped with a dual-belt treadmill, a two-degree of freedom platform, and a 180° cylindrical screen where virtual environments are projected and synchronized with the treadmill and the subject. A Vicon motion-capture system (Oxford Metrics, Oxford, UK) equipped with 10 optoelectronic cameras (sample frequency 100 Hz) surrounds the system. Subjects interact with virtual environments with their movement, thanks to passive markers located in different body parts depending on the activity. The system returns visual, proprioceptive and auditory feedback to the subject to support rehabilitation.
The VRTT included exercises to improve walking and balance abilities in engaging VR environments, for example, by displaying in real-time the joints kinematic during walking through a forest or by transferring load from one body side to the other to avoid obstacles while practicing ski. The training was highly personalized for the motor and cognitive performance of each patient. Experienced physiotherapists, trained and certified by Motek, defined and performed the training sessions on the GRAIL system.
The VRTT included exercises to improve walking and balance abilities in engaging VR environments, for example, by displaying in real-time the joints kinematic during walking through a forest or by transferring load from one body side to the other to avoid obstacles while practicing ski. The training was highly personalized for the motor and cognitive performance of each patient. Experienced physiotherapists, trained and certified by Motek, defined and performed the training sessions on the GRAIL system.
4
Whack-a-Mole Stepping Coordination Task
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Participants stood on a home position (two 18 cm × 11 cm felt mats) and performed a whack-a-mole type game with sequential foot stepping. Six stepping targets (six 12 cm × 12 cm felt mats) were positioned to the front, back, and side of the home position (see Figure 1 ). The distances between the targets and home position were marked at 60% of the longest step that the participants were able to accomplish in each direction. The step length to each of the six blocks, therefore, was comfortable and less than maximum leg reach. Six visual cues (i.e., six holes) were presented on the monitor positioned in front of the participants. These visual cues were spatially compatible with the targets on the floor. One mole at a time successively popped up from one of the six holes to represent the sequence order (see Figure 1 ). A laptop computer with a customized Labview (National Instruments, Austin, TX, USA) program controlled the sequential stimuli. A Vicon motion capture system (Oxford Metrics, Oxford, UK) recorded the real-time three-dimensional positions of reflective markers attached to the participants’ big toes, heels (calcaneus), and the 5th metatarsal on both feet with a sampling frequency of 200 Hz.
5
Gait Kinematics and Stepping Accuracy
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The following motor performance variables were calculated: (i) time to complete the walking trial(s); (ii) stance duration preceding the first and second target; and (iii) stepping error (mm) in both anterior–posterior (AP) and mediolateral (ML) directions for the first and second target. Kinematic data were collected at 100 Hz using a Vicon motion capture system (Oxford Metrics, England) and passed through a low-pass Butterworth filter with a cutoff frequency of 5 Hz (4 (link),9 (link)). Time to complete the walking trial was calculated as the time between the “go” tone and heel contact of the final step on the walkway. “Stance durations” were defined as the duration between heel contact and toe-off of the foot performing the target step. Stepping error was calculated by subtracting the coordinate of the mid-foot marker from the coordinate of the center of the target, in AP and ML directions, respectively (4 (link),9 (link)). Data were analyzed using custom algorithms in MATLAB, version 7.11 (MathWorks, Natick, MA). Kinematic data were assigned a randomized code to allow for blinded analysis, and variables were averaged across conditions. Because of technical limitations, one high-risk participant was excluded from kinematic data analyses.
6
Detailed Hand Kinematics Measurement
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The hand kinematics was measured using a Vicon motion capture system (Oxford Metrics Ltd., Oxford, UK) due to its high accuracy (0.1mm). The system is comprised of 14 MX40 Vicon cameras collecting frames at 120Hz. To capture the hand movements, 20 9.5mm reflective markers were placed on the subjects’ dominant hand. As shown in Fig 1A , 15 markers are located on the middle of each of the phalanges, another three on the metacarpals representing the surface of the palm, and another two on the wrist joint, one on the scaphoid bone and one on the triquetral bone. The trajectories data collected by the Vicon were filtered with a 15Hz low pass filter to reduce noise.
7
Adaptive Gait Targeting Protocol
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Participants walked on a force platform instrumented treadmill (custom-built, ForceLink, Culemborg, The Netherlands) equipped with a projector and C-Mill software (Cuefors, ForceLink, Culemborg, The Netherlands), allowing the projection of stepping targets onto the belt’s surface based on online detected gait events (using center of pressure (COP) data, sampled at 1000 Hz; Roerdink et al. 2008 (link)). In addition, reflective markers were attached to both shoes with two markers mounted on each shoe along the AP axis of the foot at heel side (at the approximate position of the calcaneal tuberosity) and toe side (at the approximate position of the second toe) to record stepping errors relative to the projected targets, using a 10-camera Vicon motion capture system (Oxford Metrics Group, Oxford, UK) at 100 Hz. During the dual-task trials, participants wore a headphone and a head-mounted microphone (wireless recording at 3000 Hz).
8
Biomechanical Gait Analysis during Targeted Walking
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Participants completed all walks while fitted with reflective markers placed on the heel, mid-foot, and first metatarsal of both feet. Kinematic data were collected at 100 Hz using a Vicon motion capture system (Oxford Metrics, England) and passed through a low-pass Butterworth filter with a cutoff frequency of 5 Hz (Ellmers et al., 2020 (link)). The following motor performance variables were calculated (Ellmers et al., 2020 (link)): (a) Time to complete the walking trial (seconds between “go” tone and heel contact of final step on walkway); (b) Stance duration preceding the first and second target (time difference between heel contact and toe-off of foot initiating target step); and (c) stepping error (mm) in both the anterior-posterior (AP) and mediolateral (ML) planes for the first target and second target (ie, difference between co-ordinates of mid-foot marker and center of the target). Data were analyzed using custom algorithms in MATLAB version 7.11 (MathWorks, Natick, MA). Kinematic data were assigned a randomized code, to allow for blinded analysis, and variables were averaged across conditions.
9
Kinematics Analysis of Human Walking
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All relevant experimental walking data are available on Dryad [ 15 ]. Kinematic data were recorded from 16 retroreflective markers placed on the head, pelvis, and feet [ 22 ]. Marker trajectories were collected at 120Hz using a 10-camera Vicon motion capture system (Oxford Metrics, Oxford, UK) and post-processed using Vicon Nexus and D-Flow software (Motek, Amsterdam, Netherlands). Marker trajectories were analyzed in MATLAB (MathWorks, Natick, MA). Heel strikes were determined using a velocity-based detection algorithm [ 29 ]. Lateral foot placements (z L and z R ) were defined as the lateral location of the heel marker at each heel strike. Step width (w) was defined as the lateral displacement between foot placements: w = z R -z L . Lateral position (z B ) was defined as the midpoint between foot placements:
10
Gait Analysis of Transtibial Amputees
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A walkway was covered with 10 VICON motion capture system (Oxford Metrics, Oxford, UK) cameras and two force plates (Kistler Group, Winterthur, Switzerland) synchronized with the motion capture system. Three-dimensional kinematic and kinetic data were collected at 120 Hz and 1200 Hz, respectively, while the participants walked along the level walkway. Force platforms were used to identify gait events and collect kinetic data. Twenty reflective markers were attached to the participants’ body landmarks based on the lower-body plug-in gait model. Surface markers were attached directly to the skin or prostheses of the amputees. For TTA subjects, the shank and foot markers on the prosthesis were approximated to match the locations of the corresponding markers on the intact side. The participants stood in an anatomical position to record their static position. We asked participants to walk barefoot at a self-selected speed on an 8-m walkway in the laboratory, and kinematic data were recorded for six gait cycles.
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